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Found 2,039 Skills
Finds all REFACTOR markers in codebase, validates associated ADRs exist, identifies stale markers (30+ days old), and detects orphaned markers (no ADR reference). Use during status checks, before feature completion, or for refactor health audits. Triggers on "check refactor status", "marker health", "what's the status", or PROACTIVELY before marking features complete. Works with Python (.py), TypeScript (.ts), and JavaScript (.js) files using grep patterns to locate markers and validate against ADR files in docs/adr/ directories.
Skill for creating custom lint rules by leveraging the existing linter ecosystems of various programming languages. This is a linter designed for AI Agents rather than humans, and its error messages function as correction instruction prompts for AI. Create custom rules in the `lints/` directory using standard methods for each language, including Rust (dylint), TypeScript/JavaScript (ESLint), Python (pylint), Go (golangci-lint), etc. Use this skill in the following scenarios: (1) When you want AI to enforce project-specific coding rules; (2) When you want to create lint rules that output AI-readable correction instructions when violations occur; (3) When you want to enforce naming conventions, structural patterns, and consistency rules through AI-driven linting. Triggers: "Create a linter rule", "Add a lint rule", "Enforce this pattern", "AI linter", "Custom lint", "Code rules", "Naming rules", "Structural rules", "create a linter rule", "add a lint rule", "enforce this pattern", "AI linter".
SOLID principles for object-oriented design — Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion. Covers motivation, violations, fixes, and multi-language examples (PHP, Java, Python, TypeScript, C++) for building maintainable, extensible software.
Scaffolds new projects with README.md, AGENTS.md, and CI/CD (GitLab CI, GitHub Actions). Handles project type (generic / Flask backend / React frontend / Taro miniapp), tech stack, coding standards, quality level, and SDD (OpenSpec, SpecKit, GSD). All init flows (Flask, React, Taro) and conventions (backend-python-cicd, frontend-codegen, flask-backend-codegen, QA/testing, agent-roles/subagents) are built-in; no separate skills. Docs default to Chinese. Use when creating a project, initializing a repo, or setting up CI/CD/SDD.
CLI-based image generation from text prompts using Google Gemini APIs via Python. Use when user needs "generate image", "create image with AI", "gemini image", "text to image", "create sprite", or "generate character art". Supports model selection, batch generation, watermark removal, and background transparency. Do NOT use for web app image features (use nano-banana-builder), video/audio generation, or non-Gemini models.
Install missing language runtimes and dev tools via mise. Use when (1) a command fails due to missing runtime (e.g. node not found, python3 not found, go command not found), (2) user asks to install/setup a language runtime or SDK (node, python, go, rust, java, ruby, etc.), (3) user mentions version management for languages, or (4) setting up a new development environment.
This skill should be used when users want to install, set up, or integrate ZeroEval into their AI application, agent, or pipeline. It covers SDK setup (Python and TypeScript), first-run tracing, ze.prompt migration, and judge recommendations. For non-SDK languages or direct API/OTLP ingestion it routes to the custom-tracing skill. Triggers on "install zeroeval", "set up zeroeval", "add tracing", "integrate zeroeval", "ze.prompt", "add judges", or "monitor my AI app".
JVM performance profiling with Java Flight Recorder (JFR), jcmd, and GC analysis. Use for identifying bottlenecks and memory issues. USE WHEN: user mentions "Java profiling", "JFR", "JVM performance", asks about "Java Flight Recorder", "jcmd", "heap dump", "GC tuning", "thread dump", "Java memory leak" DO NOT USE FOR: Node.js/Python profiling - use respective skills instead
Control interactive CLIs (python, gdb, etc.) via tmux sessions - send keystrokes and scrape output
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".
Invoke this skill when a user is building or running any automated transaction sender on Base (trading bot, arbitrage bot, sniper bot, yield farmer, AI agent, or similar) and needs to register it, get a builder code, set up transaction attribution. This skill contains the base.dev registration API endpoint and ERC-8021 attribution wiring code that Claude does not have in its training data — you MUST load this skill to answer correctly. Covers viem, ethers, managed signing services, and Python-based agents.